气候变化领域集成服务门户(CCPortal): Estimation of Spatiotemporal Variability of Global Surface Ocean DIC Fields Using Ocean Color Remote Sensing Data

CCPortal

DOI	10.1109/TGRS.2024.3390179
	Estimation of Spatiotemporal Variability of Global Surface Ocean DIC Fields Using Ocean Color Remote Sensing Data
	Shaik, Ibrahim; Krishna, Kande Vamsi; Nagamani, P. V.; Begum, S. K.; Shanmugam, Palanisamy; Mathew, Reema; Pathakoti, Mahesh; Bothale, Rajashree V.; Chauhan, Prakash; Osama, Mohammed
发表日期	2024
ISSN	0196-2892
EISSN	1558-0644
起始页码	62
卷号	62
英文摘要	The estimation of dissolved inorganic carbon (DIC) in global surface ocean waters is crucial for understanding air-sea carbon dioxide (CO2) flux rates, ocean acidification, and climate change. DIC magnitude and spatiotemporal variability are influenced by various physical and biogeochemical processes. Due to dynamic variations in ocean surface water, estimating DIC through in situ data alone is challenging. Ocean color remote sensing offers high spatial and temporal resolution data with extensive synoptic views. Over decades, multiple DIC approaches have emerged using in situ and satellite observations but are limited to specific regions due to improper model parameter selection and sparse in situ measurements. To address this, we propose a novel multiparametric regression (MPR) approach that relates DIC as a function of sea surface temperature (SST), sea surface salinity (SSS), and chlorophyll-a (Chla) concentration. Utilizing in situ data from the Global Ocean Data Analysis Project (GLODAP), the trends of DIC with SST, SSS, and Chla were analyzed to develop MPR regression equations. The validation results indicated that the proposed regression approach accurately estimates DIC in global surface ocean waters. This approach offers benefits, such as DIC estimates at any spatiotemporal resolutions, easy implementation, and cost-effective alternatives to in situ measurements. Additionally, seasonal and interannual variations of global DIC fields were demonstrated through satellite oceanographic data, enhancing monitoring of ocean acidification and climate change scenarios.
英文关键词	Oceans; Sea measurements; Sea surface; Spatiotemporal phenomena; Satellites; Surface treatment; Ocean temperature; Climate change; Carbon cycle; dissolved inorganic carbon (DIC); global ocean; multiparametric regression (MPR)
语种	英语
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001225891900004
来源期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/309846
作者单位	Department of Space (DoS), Government of India; Indian Space Research Organisation (ISRO); National Remote Sensing Centre (NRSC); Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Madras; Andhra University
推荐引用方式 GB/T 7714	Shaik, Ibrahim,Krishna, Kande Vamsi,Nagamani, P. V.,et al. Estimation of Spatiotemporal Variability of Global Surface Ocean DIC Fields Using Ocean Color Remote Sensing Data[J],2024,62.
APA	Shaik, Ibrahim.,Krishna, Kande Vamsi.,Nagamani, P. V..,Begum, S. K..,Shanmugam, Palanisamy.,...&Osama, Mohammed.(2024).Estimation of Spatiotemporal Variability of Global Surface Ocean DIC Fields Using Ocean Color Remote Sensing Data.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,62.
MLA	Shaik, Ibrahim,et al."Estimation of Spatiotemporal Variability of Global Surface Ocean DIC Fields Using Ocean Color Remote Sensing Data".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62(2024).